Сравнение методов

	Робастный анализ соответствий	Робастный множественный анализ соответствий (Robust MCA)
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	2000s (robust extensions of CA developed since the early 2000s)	2000s
Автор метода	Greenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleagues	Extensions by Hubert, Rousseeuw and collaborators; building on classical MCA by Benzécri (1973) and Greenacre (1984)
Тип	Robust dimension reduction for contingency tables	Robust multivariate dimension reduction
Основополагающий источник	Croux, C. & Ruiz-Gazen, A. (2005). High breakdown estimators for principal components: the projection-pursuit approach revisited. Journal of Multivariate Analysis, 95(1), 206–226.	Greenacre, M. J. (2017). Correspondence Analysis in Practice (3rd ed.). Chapman & Hall / CRC Press, Boca Raton. ISBN: 978-1498731775
Другие названия	RCA, outlier-resistant correspondence analysis, robust CA	Robust MCA, Outlier-resistant MCA, Robust HOMALS
Связанные	5	4
Сводка	Robust Correspondence Analysis (RCA) extends classical correspondence analysis to contingency tables that contain outlying rows or columns. By replacing the standard singular value decomposition with a robust alternative, RCA produces biplots and coordinate maps that accurately reflect the dominant association structure even when atypical cells or categories exert undue influence on the standard solution.	Robust Multiple Correspondence Analysis extends classical MCA to datasets containing outlying or atypical rows of categorical data. By downweighting influential observations before the singular value decomposition, it produces a low-dimensional map of category relationships that faithfully represents the bulk of the data rather than being distorted by a handful of anomalous cases.